Symbolic Computation for Statistical Inference

“Symbolic Computation for Statistical Inference” by David F. Andrews, published by Oxford University Press in 2000, is a comprehensive exploration of the advancements in statistical computing. This 157-page book discusses how developments in symbolic computing have transformed the landscape of statistical analysis, enabling researchers to manipulate algebraic expressions rather than relying solely on numerical evaluations. The text highlights the evolution of algorithms that address standard undergraduate problems and progresses to more sophisticated tools applicable in various statistical methodologies.
Readers will find a detailed summary of a decade’s worth of research focused on the application of symbolic computation to statistical inference challenges. The book illustrates the potential of these methods to automate statistical calculations, allowing researchers to shift their focus toward innovative concepts. By integrating topics such as mathematics, probability, and numerical analysis, this edition serves as a valuable resource for those interested in the intersection of computing and statistical methodology.
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Over recent years, developments in statistical computing have freed statisticians from the burden of calculation and have made possible new methods of analysis that previously would have been too difficult or time-consuming. Up till now these developments have been primarily in numerical computation and graphical display, but equal steps forward are now being made in the area of symbolic computing: the use of computer languages and procedures to manipulate expressions. This allows researchers to compute an algebraic expression, rather than evaluate the expression numerically over a given range. This book summarizes a decade of research into the use of symbolic computation applied to statistical inference problems. It shows the considerable potential of the subject to automate statistical calculation, leaving researchers free to concentrate on new concepts. Starting with the development of algorithms applied to standard undergraduate problems, the book then goes on to develop increasingly more powerful tools. Later chapters then discuss the application of these algorithms to different areas of statistical methodology.
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